The use of sigmoid gating in DeepSeek-V3 offers several key benefits, particularly in the context of Mixture-of-Experts (MoE) models. Here are the detailed advantages:
1. Decoupling Router Scores: Unlike traditional softmax gating, which normalizes scores across all experts, sigmoid gating allows each expert's score to be computed independently. This decouples the router scores, meaning that the activation of one expert does not directly influence the activation of others. This can lead to more flexible and nuanced routing decisions, as each expert's score is not constrained by the others' scores[5].
2. Avoiding Over-Confidence: Softmax gating can sometimes lead to over-confidence in the routing decisions, where a single expert is chosen with very high probability, potentially ignoring other relevant experts. Sigmoid gating mitigates this by allowing multiple experts to be activated with high probabilities, promoting a more balanced utilization of experts across the model[5].
3. Preserving Expert Contributions: By using sigmoid gating, the contribution of each expert is preserved more effectively. The gating values, which are multiplied with the expert outputs, are derived from the original affinity scores without normalization. This ensures that the integrity of each expert's contribution is maintained, even when multiple experts are activated[3].
4. Flexibility in Routing: Sigmoid gating provides more flexibility in routing decisions, as it does not enforce a strict normalization across all experts. This flexibility can be particularly beneficial in scenarios where multiple experts are equally relevant for processing a given input token, allowing the model to leverage diverse knowledge sources more effectively[5].
5. Reduced Risk of Routing Collapse: Routing collapse occurs when the model consistently favors a small subset of experts, effectively reverting to a dense model. Sigmoid gating, combined with other load-balancing strategies like dynamic bias terms, helps prevent this by encouraging a more balanced distribution of tokens across experts without directly penalizing imbalance through auxiliary losses[3].
Overall, the use of sigmoid gating in DeepSeek-V3 enhances the model's ability to manage expert specialization and knowledge sharing efficiently, contributing to its strong performance and computational efficiency.
Citations:
[1] https://community.aws/content/2rJj1WkztSfYwVfsIibhWxeqMf1/four-unique-takeaways-from-deepseek-v3?lang=en
[2] https://ai.gopubby.com/deepseek-v3-explained-2-deepseekmoe-106cffcc56c1
[3] https://machinelearningatscale.substack.com/p/deepseek-v3-model
[4] https://www.linkedin.com/posts/niccolo-gentile-phd-02208160_there-is-understandably-quite-a-lot-of-activity-7289951426699493376-Q1Ob
[5] https://mlfrontiers.substack.com/p/understanding-deepseek-v3
[6] https://metr.org/blog/2025-02-14-measuring-automated-kernel-engineering/
[7] https://arxiv.org/html/2412.19437v1
[8] https://stackoverflow.com/questions/56318636/what-are-the-benefits-of-using-a-sigmoid-function